Seismic data recorded for the purpose of imaging subsurface reflections are contaminated by seismic waves that travel along the earth's surface or along the ocean bottom. These high-amplitude noises, called surface waves, guided waves, or ground roll, must be removed so that the weaker reflections can be imaged or used to interpret physical structure or properties of the earth. The conventional approach is to design and apply digital signal-processing filters with the goal of reducing the noise but retaining the signal. There are many such filters, some are single-trace filters and some are multi-trace filters, but they all exploit a difference in the digital recordings for surface-wave noise and for reflection signals. Unfortunately, there is not a single characteristic that uniquely distinguishes surface waves and reflections, and such filters remove some reflection signal along with the noise. In addition, the performance of signal-processing filters degrades when the behaviors of the surface-waves are complex and change laterally over the survey area and when the distances between receivers are too long or too irregular as is typically found with 3D seismic surveys.
The effect of removing part of the reflections or leaving residual noise is to limit the ability to use the seismic data for determining the detailed physical structure and properties of a subsurface earth region. In particular, the ability to use the data for assessing the hydrocarbon potential of a prospective reservoir and for optimizing the well placement for extraction of the hydrocarbons is limited. One of the common problems from inadequate surface-wave removal is the lack of good amplitude fidelity of the processed seismic data. Amplitude attributes of processed data can be used to show subtle changes in subsurface structure or properties due to varying rock type or bed thickness and the presence of small channels or faults. In addition, the reflection amplitudes as a function of reflection angle can be to determine reservoir properties, such as porosity, shale content, or the presence of oil or gas. These methods include AVO (amplitude versus offset) analysis and inversion for prestack or limited-offset stack data. Such methods are typically more successful for data acquired in deep marine areas, where surface wave noise is less of a problem, than for data acquired on land or in shallow marine areas. For the later, surface wave noise can mask the reflection amplitudes and conventional processing methods cannot remove the noise without adversely affecting the integrity of amplitudes of the reflections. Sophisticated AVO inversion methods for reservoir property estimation, in particular, need high-integrity low-frequency reflection amplitudes at different source- and receiver offset distances (reflection angles) and are particularly affected by poorly mitigated surface wave noise.
Conventional Filtering Methods
The simplest single-trace filter used to mitigate ground roll or surface waves is a low-cut frequency filter. Because ground roll is typically lower in frequency than reflectors it can be removed by filtering out the lower frequencies in the recordings. Unfortunately, the low-frequency components of the reflections are also removed, and along with them, the ability for deep imaging and for sophisticated reservoir property analyses. More recently, polarization filters have been developed using earthquake-seismology methods. Polarization filters (Pinnegar, PCT Patent Application Publication No. WO 2007/006145) require 3-component receivers and exploit the fact that the relationship between vertical and horizontal components, or the polarization, is different for Rayleigh waves (the fundamental low-velocity surface wave) than for reflections. However, the success of polarization filtering for seismic imaging applications is limited because the surface-wave energy does not consist of only simple Rayleigh waves but rather a mix of Rayleigh waves and other higher-order, faster modes of surface waves that do not have distinctive polarization characteristics compared to signal. Unlike earthquake seismology, the source and receiver distances are too short for the wave types to separate in time. The ground-roll waveforms consist of the interference of several different types of surface waves along with converted waves and reflections. Such interference makes the computation of polarization ambiguous and limits the ability to remove surface waves and retain reflections.
The majority of ground-roll mitigation methods are multi-trace filters that exploit velocity differences; surface waves are typically much slower in velocity than P-wave reflections, and they have more time move out from trace to trace. Different methods exploit this difference by velocity filtering, FK filtering, adaptive filtering, or beam forming (see, for example, U.S. Pat. No. 6,651,007 to Ozbek). A problem with such methods is that they require a sufficiently short distance between each of the receivers so that the recorded ground roll is not aliased, i.e. so that the ground roll is adequately sampled. If the distance is too large, then the apparent ground-roll velocity is ambiguous at some frequencies, and both ground roll and reflectors are removed.
Additional problems arise with multi-trace velocity filters because of the complexity and variability of the near-surface and surface-wave behaviors. Because the near-surface properties change rapidly with depth, the ground-roll velocity is dispersive, and the surface-wave velocity changes with frequency. In addition, multiple modes of the ground-roll exist each with different velocity and dispersion characteristics. Velocity filters must handle a range of velocities or be run multiple times with different velocities. With broader filters or multiple passes, more reflection energy can be harmed. Furthermore because of attenuation, the ground-roll amplitude is decreasing rapidly with larger source to receiver distances, and the multi-trace filter performance is reduced. For example, with phase-match filtering (U.S. Pat. No. 5,781,503 to Kim), the ground-roll noise for all offsets is aligned with a specific velocity dispersion relation, and a horizontal averaging filter is used to extract the ground roll and leave reflections. Because the ground-roll changes amplitude laterally, not all of the ground-roll is extracted by averaging a group of traces. Finally, the properties of the near-surface and thus the velocities of the ground roll can change laterally requiring changing filter parameters and limiting the effectiveness of the filters. Such velocity changes can be difficult to estimate, because two-dimensional transforms, such as ƒ-k transforms or radon transforms, effectively average over all of the receiver traces in the gather.
Inverse Methods
In two published papers, Ernst et al. (“Tomography of dispersive media” J. Acoust. Soc. Am. 108, 105-116 (2000); and “Removal of scattered guided waves from seismic data,” Geophysics 67, 1240-1248 (2002)) model and then predict both direct and scattered surface waves using inversion. The surface-waves are subtracted from the data. They make a number of approximations and thus various corrections are needed. In addition, the surface-wave is removed by adaptive subtraction with a final trace-by-trace phase and amplitude adjustments. The results on field data are not substantially better than standard ƒ-k filtering (see the 2002 Ernst et al. paper). The prediction is done in two steps. First a tomographic inversion is used to invert generalized traveltimes to obtain a laterally-varying phase-velocity field. Then a second inversion is done to find locations of scatterers. The inversions are done only for kinematic effects, i.e. assuming acoustic velocities, and amplitudes (dynamic effects) are not directly included within the tomographic method.
The primary assumption and limitation in the Ernst tomographic method is that it must use a time window with only one ground-roll mode. This requires that the source and receivers be sufficiently far apart so that the modes are well separated in time. This condition is met in earthquake seismology, but is not the case for seismic imaging applications. The presence of other events, such as other surface-wave modes, will distort the phase-velocity estimation. Another simplification is the use of generalized traveltimes, which involves computing the derivative of the phase of the data and problems with phase unwrapping. The solution is nonunique and needs more a priori information. Their method to obtain the a priori information is to obtain a phase correction by back propagating all the receivers to the source position, stacking and maximizing the semblances, ignoring receiver coupling and lateral variations. Next they solve for the source waveform by back propagating all traces to the source and averaging the traces, ignoring variable attenuation. Finally for field data, they estimate a single attenuation quality factor, independent of frequency and lateral position. Next the data are used to invert for locations of scatters, the resulting scattered waves predicted and adaptively subtracted from the data. Assuming only one mode, they sequentially estimate the parameters for laterally varying propagation velocities, a source-phase correction, source wavelet spectra, and a constant attenuation quality factor and scattering locations.
Interferometry Methods
Recently it has been postulated that interferometric methods might be used to estimate the full wavefield of the surface-wave and subtract it from seismic data (U.S. Patent Application Publication No. 2007/0104028 by Van Manen et al.; and Xue et al., “Surface wave elimination by interferometry with nonlinear local filter,” SEG Expanded Abstracts 26, 2620-2624 (2007)). The interferometric operations are simple to compute. They involve cross correlating the data from a pair of receivers and stacking over all sources. The action of cross correlation is equivalent to convolution or filtering with the time reverse of one of the signals. Theoretically, the Green's function between two receiver stations is obtained, the Green's function being a seismogram with a delta function as the source at one of the receiver stations. However, the theory is only for a lossless earth (no attenuation), and for sources that completely surround the object (Curtis et al., “Seismic interferometry—turning noise into signal,” The Leading Edge 25, 1082-1092 (2006)). When these conditions are not met, the amplitude (dynamic) part of the wavefield will not be correct. Another part of the limitation are the requirements for source locations such as on a perimeter of the survey or so that they are at points of stationary phase for the surface waves but not for reflectors. The latter is required for distinguishing surface-waves and reflections. There are a number of issues with this method that have not been resolved, particularly with 3D data.
Thus there remains a need to correct the seismic data by predicting and subtracting surface-waves or ground roll from seismic data without harming reflections and without requiring fine and uniform spacing of sources or receivers. The method should encompass the full complexity (multimodal, laterally variant, etc.) of surface waves and both kinematic (velocity) and dynamic (amplitude) effects. It is also desirable that problems, such as computational intensity, non-linearity, non-uniqueness and non-physical approximations associated with solving for a full earth model as function of depth, be avoided. The corrected data should have good fidelity low frequency reflection amplitudes and be suitable to be used for determining the physical structure or physical properties of a subsurface region. The present invention satisfies these needs.